Damien Teney

24

Papers

39

Total Citations

Papers (24)

Neural Redshift: Random Networks are not Random Functions

Bayesian Low-Rank Learning (Bella): A Practical Approach to Bayesian Neural Networks

Synergy and Diversity in CLIP: Enhancing Performance Through Adaptive Backbone Ensembling

Learning Similarity Metrics for Dynamic Scene Segmentation

Graph-Structured Representations for Visual Question Answering

Vision-and-Language Navigation: Interpreting Visually-Grounded Navigation Instructions in Real Environments

Tips and Tricks for Visual Question Answering: Learnings From the 2017 Challenge

Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering

Actively Seeking and Learning From Live Data

Counterfactual Vision and Language Learning

Evading the Simplicity Bias: Training a Diverse Set of Models Discovers Solutions With Superior OOD Generalization

Active Learning by Feature Mixing

Image Retrieval on Real-Life Images With Pre-Trained Vision-and-Language Models

Unshuffling Data for Improved Generalization in Visual Question Answering

Beyond Question-Based Biases: Assessing Multimodal Shortcut Learning in Visual Question Answering

Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision

Predicting Is Not Understanding: Recognizing and Addressing Underspecification in Machine Learning

Do We Always Need the Simplicity Bias? Looking for Optimal Inductive Biases in the Wild

Mysteries of the Deep: Role of Intermediate Representations in Out of Distribution Detection

Selective Mixup Helps with Distribution Shifts, But Not (Only) because of Mixup

On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law

Counterfactual Vision-and-Language Navigation: Unravelling the Unseen

SelecMix: Debiased Learning by Contradicting-pair Sampling

ID and OOD Performance Are Sometimes Inversely Correlated on Real-world Datasets